The Underlying Model.  The idea of weak supervision using a static analyzer could be developed on top of many different kinds of models. Here, we develop the idea on top of a model from [1]}. This model uses a latent variable \({Z}\)  to represent the true user intent behind the incomplete or ambiguous evidence set \(Y\) . We then have \(\mathcal {P}(\mathsf {Y}|\mathsf {X}) = \int _\mathsf {Z}\mathcal {P}(\mathsf {Z}|\mathsf {X}) \mathcal {P}(\mathsf {Y}|\mathsf {Z}) d\mathsf {Z}. \) 
To define the distribution \(\mathcal {P}({Z}|\mathsf {X})\) , we assume that the evidence set has data of a fixed number of types—e.g., method names, formal parameters, and Javadoc comments.
